Asked about the industry’s reactions to this report, Loudiadis says, “Although we’ve shared this report only with a few network service providers so far, they’re loving it.” App developers called the report “an eye-opener,” while hardware box designers are calling it “huge,” she adds.

Stressing that Alcatel-Lucent, too, is a hardware box vendor, Loudiadis says, “Our hardware guys use it to understand the future of networks, [their] growth rate, and the increasing load their systems need to handle.”

Key metrics
To create the analytics in this report, Alcatel-Lucent measured two key metrics: “data volume” and “signaling consumption” -- each demanded by different mobile apps. In its view, both metrics are critical in probing the impact of the mobile apps’ misbehavior.

But how exactly do these analytics help companies in the mobile world improve their business? Loudiadis shared with us one high-profile case: Facebook.

Facebook’s ambition to expand its business to the mobile world is well known. At the Mobile World Congress earlier this year Facebook CEO Mark Zuckerberg urged mobile operators to zero-rate Facebook. He told operators that Facebook can bring the Internet to the next 2 billion users, concurrently boosting operators’ profits.

The argument wouldn’t have flown had Facebook been stuck where it was a year ago. It turns out Facebook last year learned that its app, unexpectedly, increased overall signaling load as much as 10% overnight with the company’s new software release. Such a spike spells bad news for operators, because they know it will cost their networks. Facebook at that time had no idea how this happened, let alone what to fix.

When Alcatel-Lucent blogged about some of the findings of the company’s analytics last year, Facebook saw the blog post, and “they called us,” said Loudiadis.

In their efforts to trace the culprit, the Alcatel-Lucent team members and Facebook staff found the signaling spike was caused by misbehavior by the app on the Android platform. Five months later, Facebook released a new Android version, which resulted in not only reversing the signaling effect, but actually improving it.

The moral of the story? “Having visibility of the apps’ impact on mobile networks is key to reducing the cost of delivery and understanding apps that could be packaged to the benefits of all parties,” the report concluded.

Framework for network impact rankingsThe report laid out the following framework -- divided in four quadrants -- illustrating how the team categorized network impact rankings.

Not clear that the chattiness is always bad. What if G map consumes less MB because it is chatty, for example maybe A buffers larger margins (so you can scroll) which wastes MB, while G might have a more responsive infrastructure so they can use narrower margins but have to send more requests?

There are two sides to this. One is the impact on the phone, one is on the network. It does not seem clear that the story on the phone is constant - couldn't the chips and OS be optimized to do the brief messages efficiently? How do the operators decide that it is the wrong strategy for battery life? It likely depends a lot on the software.

Then the other side is how well the operators are organized for chattiness. Long data flows are relatively easy in their backhaul. Short messages are a nuisance because they have no steady pace. They also may have some issues in how they configure the wireless signalling. A decade ago the operators in NA had huge headaches with SMS because they did not anticipate the demand and set up their cells with a correct traffic allocation. Are they doing the same again? I assume there are aspects of the 3G and 4G protocols which require that transient messages (chats) compete on signaling and scheduling with predictable long data flows, and that is what really bugs them here?

It would be interesting to have guidance for network impact of different signalling patterns, for example how the networks assume the balance of short, medium, and bulk messages will be in the traffic when they set up the network, and what the cost in latency or overall capacity is if these fractions change.

I see your point Junko...not sure whether I agree...the networks operators might claim they don't want to build more infrastructure to handle bandwidth...but their pricing plans seem to indicate otherwise...here in Vancouver I can get 1Gb data for $30, 2 Gb for $35 and 10 Gb for $40, and 100 Gb for $50...this does not scale! it is more logarithmis function...they actively encourage me to consume more bandwidth...(no luck with me as I am having difficulty filling in 1 Gb a month, I am a sissy, I know)...Kris

It would be interesting to get a normalized listing of these different apps, so you get a better idea of how well they are written. Obviously, a very popular app will create a greater overall load, but an elegantly written app has its own appeal.

We need obsessively compulsive designers, who don't mind going back to work the next day and fix what they did yesterday, which has kept them on edge all night. And managers who understand why this is important. In the rush to get things out, there's a lot of half-*ssed work going un-optimized, I'm afraid.

So in spite of Comcast's much-advertized worries, Netflix creates a little less data volume than YouTube, and is much better than most of the listed apps for signaling efficiency. Not bad, I'd say, for a service that streams HD movies.